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Technical Abstract: New molecular markers are being designed and validated for grain quality improvement based on computationally assisted analysis of genome wide association study (GWAS) findings across multiple panels and multiple grain quality traits. The traits include grain dimensions, apparent amylose content (AAC), alkali spreading value (ASV), protein content, and chalk percent. The markers utilize the Kompetitive Allele Specific PCR (KASP) SNP technology. Discovery of SNPs associated with grain quality was accomplished using the USDA rice core subset (RCS) and mini-core (MC) GWAS panels. High density genotyping in these panels comes from public re-sequencing data on the MC and simple sequence repeat (SSR) markers on the RCS. In addition, public single nucleotide polymorphism (SNP) data from the High Density Rice Array (HDRA) genotyping of rice diversity panels 1 and 2 were merged with resequencing data from the MC which included 189 RCS accessions to yield a combined dataset of 383 individuals and 122,102 SNPs. Perl scripts were developed to define significant chromosomal regions from p-values and allele effects of the GWAS and find overlapping segments across traits and panels, and assist in candidate gene identification by summarizing curated known genes in those regions. By comparing GWAS across traits, it was possible to determine if a segment influenced a single grain quality trait or multiple traits, and whether there was a likely candidate gene for that region. From those results, significant SNPs were targeted for KASP marker development. These markers are now being validated in breeding lines and diverse collections.